To model species sensitivity distribution, we carefully select the parameters in the package BNPdensity.
Given that concentrations vary on a wide range, it is common practice to work
on log-transformed concentrations.
We choose a fully nonparametric model using the normalised stable process [1]} as mixing random measure (hence setting Alpha = 1 and Beta = 0).
We favor this process over the more classical Dirichlet process because it allows specifying less informative prior on the number of components, which makes it more robust to model misspecification [2]}.
With this process, the amount of information from the prior is controlled by the stability parameter \(\gamma \) , which we set to 0.4 (Gama = 0.4).
This choice reflects a compromise between model flexibility (\(\gamma \rightarrow 1\) ) and computational effort (\(\gamma \)  small, see also sec:package).
As we wish the location parameter of the clusters \(\mu \)  to be estimated freely, we use the default weakly informative prior of a normal base measure \(f_{0}^1(\mu |\varphi ) = \mathcal {N}(\mu |\varphi _1, \varphi _2)\)  with hyperpriors on \(\varphi \)  given by \(f(\varphi ) = \mathcal {N}(\varphi _1|\psi _1, \psi _2)\text{ga}(\varphi _2|\psi _3, \psi _4)\)  (see also [2]} for more details).
